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  <section id="torch-tensorrt-torch-compile-backend">
<span id="torch-compile"></span><h1>Torch-TensorRT <cite>torch.compile</cite> Backend<a class="headerlink" href="#torch-tensorrt-torch-compile-backend" title="Permalink to this headline">¶</a></h1>
<span class="target" id="module-torch_tensorrt.dynamo"></span><p>This guide presents the Torch-TensorRT <cite>torch.compile</cite> backend: a deep learning compiler which uses TensorRT to accelerate JIT-style workflows across a wide variety of models.</p>
<section id="key-features">
<h2>Key Features<a class="headerlink" href="#key-features" title="Permalink to this headline">¶</a></h2>
<p>The primary goal of the Torch-TensorRT <cite>torch.compile</cite> backend is to enable Just-In-Time compilation workflows by combining the simplicity of <cite>torch.compile</cite> API with the performance of TensorRT. Invoking the <cite>torch.compile</cite> backend is as simple as importing the <cite>torch_tensorrt</cite> package and specifying the backend:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Many additional customization options are available to the user. These will be discussed in further depth in this guide.</p>
</div>
<p>The backend can handle a variety of challenging model structures and offers a simple-to-use interface for effective acceleration of models. Additionally, it has many customization options to ensure the compilation process is fitting to the specific use case.</p>
</section>
<section id="customizeable-settings">
<h2>Customizeable Settings<a class="headerlink" href="#customizeable-settings" title="Permalink to this headline">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.CompilationSettings">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">CompilationSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision:</span> <span class="pre">torch.dtype</span> <span class="pre">=</span> <span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops:</span> <span class="pre">typing.Set[str]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_aux_streams:</span> <span class="pre">typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_level:</span> <span class="pre">typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_python_runtime:</span> <span class="pre">typing.Optional[bool]</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device:</span> <span class="pre">torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_settings.html#CompilationSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.CompilationSettings" title="Permalink to this definition">¶</a></dt>
<dd><p>Compilation settings for Torch-TensorRT Dynamo Paths</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>precision</strong> (<em>torch.dpython:type</em>) – Model Layer precision</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Whether to print out verbose debugging information</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Workspace TRT is allowed to use for the module (0 is default)</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – Minimum number of operators per TRT-Engine Block</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>Sequence</em><em>[</em><em>str</em><em>]</em>) – Sequence of operations to run in Torch, regardless of converter coverage</p></li>
<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Whether to fail on TRT engine build errors (True) or not (False)</p></li>
<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum number of allowed auxiliary TRT streams for each engine</p></li>
<li><p><strong>version_compatible</strong> (<em>bool</em>) – Provide version forward-compatibility for engine plan files</p></li>
<li><p><strong>optimization_level</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Builder optimization 0-5, higher levels imply longer build time,
searching for more optimization options. TRT defaults to 3</p></li>
<li><p><strong>use_python_runtime</strong> (<em>Optional</em><em>[</em><em>bool</em><em>]</em>) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime
based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the
argument as None</p></li>
<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Whether to truncate int64/float64 TRT engine inputs or weights to int32/float32</p></li>
<li><p><strong>use_fast_partitioner</strong> (<em>bool</em>) – Whether to use the fast or global graph partitioning system</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Whether to enable all core aten decompositions
or only a selected subset of them</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="../py_api/torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a>) – GPU to compile the model on</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Whether to require the graph is fully compiled in TensorRT.
Only applicable for <cite>ir=”dynamo”</cite>; has no effect for <cite>torch.compile</cite> path</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<section id="custom-setting-usage">
<h3>Custom Setting Usage<a class="headerlink" href="#custom-setting-usage" title="Permalink to this headline">¶</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;precision&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
                                         <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
                                         <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;torch.ops.aten.sub.Tensor&quot;</span><span class="p">},</span>
                                         <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
                                         <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,})</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Quantization/INT8 support is slated for a future release; currently, we support FP16 and FP32 precision layers.</p>
</div>
</section>
</section>
<section id="compilation">
<h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2>
<p>Compilation is triggered by passing inputs to the model, as so:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="c1"># Causes model compilation to occur</span>
<span class="n">first_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>

<span class="c1"># Subsequent inference runs with the same, or similar inputs will not cause recompilation</span>
<span class="c1"># For a full discussion of this, see &quot;Recompilation Conditions&quot; below</span>
<span class="n">second_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="after-compilation">
<h2>After Compilation<a class="headerlink" href="#after-compilation" title="Permalink to this headline">¶</a></h2>
<p>The compilation object can be used for inference within the Python session, and will recompile according to the recompilation conditions detailed below. In addition to general inference, the compilation process can be a helpful tool in determining model performance, current operator coverage, and feasibility of serialization. Each of these points will be covered in detail below.</p>
<section id="model-performance">
<h3>Model Performance<a class="headerlink" href="#model-performance" title="Permalink to this headline">¶</a></h3>
<p>The optimized model returned from <cite>torch.compile</cite> is useful for model benchmarking since it can automatically handle changes in the compilation context, or differing inputs that could require recompilation. When benchmarking inputs of varying distributions, batch sizes, or other criteria, this can save time.</p>
</section>
<section id="operator-coverage">
<h3>Operator Coverage<a class="headerlink" href="#operator-coverage" title="Permalink to this headline">¶</a></h3>
<p>Compilation is also a useful tool in determining operator coverage for a particular model. For instance, the following compilation command will display the operator coverage for each graph, but will not compile the model - effectively providing a “dryrun” mechanism:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">),})</span>
</pre></div>
</div>
<p>If key operators for your model are unsupported, see <a class="reference internal" href="../contributors/fx_converters.html#dynamo-conversion"><span class="std std-ref">Dynamo Converters</span></a> to contribute your own converters, or file an issue here: <a class="reference external" href="https://github.com/pytorch/TensorRT/issues">https://github.com/pytorch/TensorRT/issues</a>.</p>
</section>
<section id="feasibility-of-serialization">
<h3>Feasibility of Serialization<a class="headerlink" href="#feasibility-of-serialization" title="Permalink to this headline">¶</a></h3>
<p>Compilation can also be helpful in demonstrating graph breaks and the feasibility of serialization of a particular model. For instance, if a model has no graph breaks and compiles successfully with the Torch-TensorRT backend, then that model should be compileable and serializeable via the <cite>torch_tensorrt</cite> Dynamo IR, as discussed in <a class="reference internal" href="dynamic_shapes.html#dynamic-shapes"><span class="std std-ref">Dynamic shapes with Torch-TensorRT</span></a>. To determine the number of graph breaks in a model, the <cite>torch._dynamo.explain</cite> function is very useful:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
<span class="o">...</span>
<span class="n">explanation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="n">model</span><span class="p">)(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Graph breaks: </span><span class="si">{</span><span class="n">explanation</span><span class="o">.</span><span class="n">graph_break_count</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
</pre></div>
</div>
</section>
</section>
<section id="dynamic-shape-support">
<h2>Dynamic Shape Support<a class="headerlink" href="#dynamic-shape-support" title="Permalink to this headline">¶</a></h2>
<p>The Torch-TensorRT <cite>torch.compile</cite> backend will currently require recompilation for each new batch size encountered, and it is preferred to use the <cite>dynamic=False</cite> argument when compiling with this backend. Full dynamic shape support is planned for a future release.</p>
</section>
<section id="recompilation-conditions">
<h2>Recompilation Conditions<a class="headerlink" href="#recompilation-conditions" title="Permalink to this headline">¶</a></h2>
<p>Once the model has been compiled, subsequent inference inputs with the same shape and data type, which traverse the graph in the same way will not require recompilation. Furthermore, each new recompilation will be cached for the duration of the Python session. For instance, if inputs of batch size 4 and 8 are provided to the model, causing two recompilations, no further recompilation would be necessary for future inputs with those batch sizes during inference within the same session. Support for engine cache serialization is planned for a future release.</p>
<p>Recompilation is generally triggered by one of two events: encountering inputs of different sizes or inputs which traverse the model code differently. The latter scenario can occur when the model code includes conditional logic, complex loops, or data-dependent-shapes. <cite>torch.compile</cite> handles guarding in both of these scenario and determines when recompilation is necessary.</p>
</section>
</section>


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